Is Lisp still being used to tackle AI problems?
Overall, the answer is no, but the current paradigms owe a lot to LISP. The language most commonly used today is python.

Relevant answers:


Stack Overflow thread explaining why LISP was thought of as the AI language: Why is Lisp used for AI
Quora answer by Peter Norvig, who wrote a popular textbook on the subject and is currently Director of Research at Google: Is it true that Lisp is highly used programming language in AI?


LISP pioneered many important concepts in what we now call functional programming, with a key attraction being how close the programs were to math. Many of these features have since been incorporated into modern languages (see the Wikipedia page). LISP is very expressive: it has very little syntax (just lists and some elementary operations on them) but you can write short succinct programs that represent complex ideas. This amazes newcomers and has sold it as the language for AI. However, this is a property of programs in general. Short programs can represent complex concepts. And while you can write powerful code in LISP, any beginner will tell you that it is also very hard to read anyone else's LISP code or to debug your own LISP code. Initially, there were also performance considerations with functional programming and it fell out of favor to be replaced by low level imperative languages like C. (For example, functional programming requires that no object ever be changed ("mutated"), so every operation requires a new object to be created. Without good garbage collection, this can get unwieldy). Today, we've learned that a mix of functional and imperative programming is needed to write good code and modern languages like python, ruby and scala support both. At this point, and this is just my opinion, there is no reason to prefer LISP over python.

The paradigm for AI that currently receives the most attention is Machine Learning, where we learn from data, as opposed to previous approaches like Expert Systems (in the 80s) where experts wrote rules for the AI to follow. Python is currently the most widely used language for machine learning and has many libraries, e.g. Tensorflow and Pytorch, and an active community. To process the massive amounts of data, we need systems like Hadoop, Hive or Spark. Code for these is written in python, java or scala. Often, the core time-intensive subroutines are written in C.

The AI Winter of the 80s was not because we did not have the right language, but because we did not have the right algorithms, enough computational power and enough data. If you're trying to learn AI, spend your time studying algorithms and not languages.